data:image/png;base64,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

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere ca:_ sense la primera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra ca:_ sense la primera paraula

All but first of empty sentence.

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere ca:_ sense la darrera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra ca:_ sense la darrera paraula

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra de _ 0

pt:a última palavra de _ ca:darrera paraula de _

Last of empty sentence.

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia ca:paraula buida? _ 0

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia ca:frase buida? _

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1

1bf

Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ (

)

Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _

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,_ ,_ ,_ 5